14 research outputs found
Two-Hop Connectivity to the Roadside in a VANET Under the Random Connection Model
We compute the expected number of cars that have at least one two-hop path to
a fixed roadside unit in a one-dimensional vehicular ad hoc network in which
other cars can be used as relays to reach a roadside unit when they do not have
a reliable direct link. The pairwise channels between cars experience Rayleigh
fading in the random connection model, and so exist, with probability function
of the mutual distance between the cars, or between the cars and the roadside
unit. We derive exact equivalents for this expected number of cars when the car
density tends to zero and to infinity, and determine its behaviour using
an infinite oscillating power series in , which is accurate for all
regimes. We also corroborate those findings to a realistic situation, using
snapshots of actual traffic data. Finally, a normal approximation is discussed
for the probability mass function of the number of cars with a two-hop
connection to the origin. The probability mass function appears to be well
fitted by a Gaussian approximation with mean equal to the expected number of
cars with two hops to the origin.Comment: 21 pages, 7 figure
Shortest Path Distance in Manhattan Poisson Line Cox Process
While the Euclidean distance characteristics of the Poisson line Cox process
(PLCP) have been investigated in the literature, the analytical
characterization of the path distances is still an open problem. In this paper,
we solve this problem for the stationary Manhattan Poisson line Cox process
(MPLCP), which is a variant of the PLCP. Specifically, we derive the exact
cumulative distribution function (CDF) for the length of the shortest path to
the nearest point of the MPLCP in the sense of path distance measured from two
reference points: (i) the typical intersection of the Manhattan Poisson line
process (MPLP), and (ii) the typical point of the MPLCP. We also discuss the
application of these results in infrastructure planning, wireless
communication, and transportation networks
How wireless queues benefit from motion: an analysis of the continuum between zero and infinite mobility
This paper considers the time evolution of a queue that is embedded in a
Poisson point process of moving wireless interferers. The queue is driven by an
external arrival process and is subject to a time-varying service process that
is a function of the SINR that it sees. Static configurations of interferers
result in an infinite queue workload with positive probability. In contrast, a
generic stability condition is established for the queue in the case where
interferers possess any non-zero mobility that results in displacements that
are both independent across interferers and oblivious to interferer positions.
The proof leverages the mixing property of the Poisson point process. The
effect of an increase in mobility on queueing metrics is also studied. Convex
ordering tools are used to establish that faster moving interferers result in a
queue workload that is smaller for the increasing-convex stochastic order. As a
corollary, mean workload and mean delay decrease as network mobility increases.
This stochastic ordering as a function of mobility is explained by establishing
positive correlations between SINR level-crossing events at different time
points, and by determining the autocorrelation function for interference and
observing that it decreases with increasing mobility. System behaviour is
empirically analyzed using discrete-event simulation and the performance of
various mobility models is evaluated using heavy-traffic approximations.Comment: Preliminary version appeared in WiOPT 2020. New version with
revision
Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach
With the increasing demand for greener and more energy efficient
transportation solutions, electric vehicles (EVs) have emerged to be the future
of transportation across the globe. However, currently, one of the biggest
bottlenecks of EVs is the battery. Small batteries limit the EVs driving range,
while big batteries are expensive and not environmentally friendly. One
potential solution to this challenge is the deployment of charging roads, i.e.,
dynamic wireless charging systems installed under the roads that enable EVs to
be charged while driving. In this paper, we use tools from stochastic geometry
to establish a framework that enables evaluating the performance of charging
roads deployment in metropolitan cities. We first present the course of actions
that a driver should take when driving from a random source to a random
destination in order to maximize dynamic charging during the trip. Next, we
analyze the distribution of the distance to the nearest charging road. This
distribution is vital for studying multiple performance metrics such as the
trip efficiency, which we define as the fraction of the total trip spent on
charging roads. Next, we derive the probability that a given trip passes
through at least one charging road. The derived probability distributions can
be used to assist urban planners and policy makers in designing the deployment
plans of dynamic wireless charging systems. In addition, they can also be used
by drivers and automobile manufacturers in choosing the best driving routes
given the road conditions and level of energy of EV battery.Comment: 25 pages, submitted to IEEE Open Journal of Vehicular Technology
(OJVT